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Vehicle Data Management System for Scenario-Based Validation of Automated Driving Functions

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1217))

Abstract

Proving the functionality of AI-controlled automated vehicles is a challenging task due to the enormous overall complexity. Although a scenario-based validation approach is widely accepted in the literature, the identification of these scenarios is still an open issue.

Real-world test drives are valuable data sources for this purpose. However, an automated system is required for data management and scenario identification to analyze the vast amount of data in a legitimate amount of time and effort. Therefore, this work proposes a modular multi-tier Vehicle Data Management System for large-scale test campaign management and analysis as the basis for scenario-based validation of automated driving functions. For system demonstration, lane-change maneuvers are identified and extracted, and an onboard DAS is evaluated with a real-world test drive sequence.

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Notes

  1. 1.

    Intelligent Validierung von Fahrerassistanzsystemen (engl.: intelligent validation of driver assistance systems) of the Hochschule Emden/Leer.

  2. 2.

    The Automotive Data and Time-triggered Framework (ADTF) of Elektrobit is used for synchronous data measurement and capturing.

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Acknowledgements

We thank LG Electronics, Vehicle Solution Company, Republic of Korea, for supporting this project by cooperating in capturing large-scale test drives and providing valuable measurement equipment.

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Correspondence to Lars Klitzke .

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Klitzke, L., Koch, C., Haja, A., Köster, F. (2021). Vehicle Data Management System for Scenario-Based Validation of Automated Driving Functions. In: Helfert, M., Klein, C., Donnellan, B., Gusikhin, O. (eds) Smart Cities, Green Technologies and Intelligent Transport Systems. SMARTGREENS VEHITS 2019 2019. Communications in Computer and Information Science, vol 1217. Springer, Cham. https://doi.org/10.1007/978-3-030-68028-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-68028-2_16

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